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Item #:
32821659
Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition
90% of respondents would recommend this to a friend
Item #:
32821659
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Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML) and provides actionable insights on the key fundamentals of ML with Python programming.
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What Stands Out
Comprehensive Learning
This edition provides a thorough exploration of machine learning concepts using Python, offering practical examples and hands-on projects that cater to beginners and experienced users alike.
Framework Diversity
Utilizing multiple frameworks like TensorFlow, PyTorch, and scikit-learn allows users to develop a versatile skill set, adapting to various machine learning applications and industry standards.
Updated Techniques
With the latest advancements and practices in machine learning, this book ensures readers are equipped with cutting-edge techniques and insights to build intelligent systems effectively.
Product Details
- Comprehensive guide to practical machine learning with Python, TensorFlow 2, PyTorch, and scikit-learn
- Exploration of cutting-edge content on deep learning and reinforcement learning
- Use of Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects
- New chapters covering topics like movie recommendation, face recognition, stock price prediction, and more
- Focus on important concepts in ML and data science using Python
- Target audience includes machine learning enthusiasts, data analysts, and data engineers with prior knowledge of Python coding
| Publisher | Packt Publishing |
| Publication date | October 30, 2020 |
| Edition | 3rd |
| Language | English |
| Print length | 526 pages |
| ISBN-10 | 1800209711 |
| ISBN-13 | 978-1800209718 |
| Item Weight | 1.97 pounds (890 grams) |
| Dimensions | 7.5 x 1.19 x 9.25 inches (19.1 x 3 x 23.5 cm) |
Who Should Buy?
-
Beginner Data Scientists
Ideal for individuals new to machine learning seeking practical examples and hands-on experience in Python.
-
Intermediate Programmers
Suited for programmers familiar with Python looking to expand their skills into machine learning concepts and applications.
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Self-Learners
Beneficial for self-motivated learners wanting to understand machine learning through real-world projects using popular libraries.
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Absolute Beginners
Not ideal for those with no programming background, as it requires prior knowledge of Python.
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Natural Language Processing Editorial Review
Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition stands out as a comprehensive guide, expertly blending theoretical knowledge with hands-on application. Readers will appreciate the book's notable approach of focusing on real-world projects, ranging from predictive modeling to sentiment analysis, ensuring practical learning (product_specification). It's tailored for those with a foundational understanding of Python and statistics, making it ideal for self-learners and classrooms (FAQ). The author's clarity in explaining complex concepts like overfitting and ensemble learning, along with well-structured code examples in popular libraries, has garnered positive reviews from readers, highlighting its approachable yet technical nature (Reviews).
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Pros
- Covers foundational to advanced topics across diverse projects
- Code examples are clean, well-commented, and reproducible
- Includes modern technologies like Transformers and CLIP
- Great for both self-learners and classroom use
- Clear explanations of complex machine learning concepts
Cons
- Assumes some prior knowledge of Python and statistics
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Features & Benefits
- Comprehensive guide to latest developments of practical machine learning with Python and upgrade understanding of ML algorithms and techniques
- Explore advanced content reflecting deep and reinforcement learning developments
- Implement ML algorithms from scratch in Python
- Gain understanding of mechanics of ML techniques and important concepts in ML and data science
- Includes realistic examples that provide insight in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP
- Intended audience - machine learning enthusiast, data analyst, or data engineer passionate about machine learning assignments with prior knowledge of Python programming
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